natural-language-processing

Environment info

• transformers version: 4.11.2
• Platform: Linux-5.4.0-66-generic-x86_64-with-glibc2.10
• Python version: 3.8.8
• PyTorch version (GPU?): 1.9.1+cu102 (True)
• Tensorflow version (GPU?): not installed (NA)
• F

Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

f-strings offer better readability/performance than str.format and %, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.

NOTE FOR CONTRIBUTORS: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under datasets/*.

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.

Hello!

I'd like to discuss a potential enhancements of NLTKWordTokenizer and TreebankWordTokenizer. For those unaware, the former is the tokenizer that is most frequently used, and is used in the word_tokenize function. It's also based on the latter class: TreebankWordTokenizer.

An example usage as can be found in the documentation:

>>> from nltk.tokenize import NLTKWor

Hey Hackers of this spoopy month! 👻
Welcome to the Ciphey repo(s)!
This issue requires you to add a decoder.

This wiki section walks you through EVERYTHING you need to know, and we've added some more links at the bottom of this issue to detail more about the decoder.
https://github.com/Ciphey/Ciphey/wiki#adding-your-own-crackers--decoders

https://www.dcode.fr/t9-cipher
https://en.wikipe